In the era of the Fourth Industrial Revolution, artificial intelligence (AI) is a core technology, and AI-based applications are expanding in various fields. This research explored the influencing factors on end-user’s intentions and acceptance of AI-based technology in construction companies using the technology acceptance model (TAM) and technology–organisation–environment (TOE) framework. The analysis of end-users’ intentions for accepting AI-based technology was verified by applying the structure equation model. According to the research results, the technological factors along with external variables and an individual’s personality had a positive influence (+) on the perceived usefulness and the perceived ease of use of end-users of AI-based technology. Conversely, environmental factors such as suggestions from others appeared to be disruptive to users’ technology acceptance. In order to effectively utilise AI-based technology, organisational factors such as the support, culture, and participation of the company as a whole were indicated as important factors for AI-based technology implementation.

The demand for categorising technology that requires minimum manpower and equipment is increasing because a large amount of waste is produced during the demolition and remodelling of a structure. Considering the latest trend, applying an artificial intelligence (AI) model for automatic categorisation is the most efficient method. However, it is difficult to apply this technology because research has only focused on general domestic waste. Thus, in this study, we delineate the process for developing an AI model that differentiates between various types of construction waste. Particularly, solutions for solving difficulties in collecting learning data, which is common in AI research in special fields, were also considered. To quantitatively increase the amount of learning data, the Fréchet Inception Distance method was used to increase the amount of learning data by two to three times through augmentation to an appropriate level, thus checking the improvement in the performance of the AI model.

The paper examines that many human resources are needed on the research and development (R&D) process of artificial intelligence (AI) and discusses factors to consider on the current method of development. Labor division of a few managers and numerous ordinary workers as a form of light industry appears to be a plausible method of enhancing the efficiency of AI R&D projects. Thus, the research team regards the development process of AI, which maximizes production efficiency by handling digital resources named ‘data’ with mechanical equipment called ‘computers’, as the digital light industry of the fourth industrial era. As experienced during the previous Industrial Revolution, if human resources are efficiently distributed and utilized, no less progress than that observed in the second Industrial Revolution can be expected in the digital light industry, and human resource development for this is considered urgent. Based on current AI R&D projects, this study conducted a detailed analysis of necessary tasks for each AI learning step and investigated the urgency of R&D human resource training. If human resources are educated and trained, this could lead to specialized development, and new value creation in the AI era can be expected.

This research addresses the factors that impact the acceptance of AI-based technologies or products depending upon firm size in the construction industry, in which various corporates exist. In order to achieve the research goals, a technology acceptance model was applied to investigate the influencing factors in respect to adopting AI-based technologies or products. From the research results, technological and organizational factors were found to positively influence perceived usefulness and perceived ease of use. Corporate users perceived that technology is useful to their work and is easy to use when enough capital and education were invested prior to the company adopting AI-based technologies or products. It was found that perceived ease of use and perceived usefulness indicate satisfaction with new technology, and the higher the intention to use, the higher the satisfaction. In addition, as various information sharing and distribution channels increase, the frequency of use of new technologies or products also increases, not through traditional marketing, but through viral marketing via social media or promotion by influential persons or organizations. Furthermore, there are differences in the adoption of AI-based technologies or products depending on the size of the company.

Construction waste generation along with the extensive consumption of natural resources has propelled researchers to investigate effective measures for minimising the waste. While several studies have shown that the structural design would be an influencing factor on the carbon dioxide emissions of a building, there is a lack of studies to corroborate the effect of different structural systems to generate waste during the construction stage. This article seeks to bridge some of the knowledge gaps regarding the waste generation from different structural systems during the construction phase in a building project in South Korea and demonstrate its potential for waste reduction. In this study, the amount of waste generation during the construction phase was calculated based on the quantities and the material loss rate of each building material to estimate the quantity of construction waste by the changes in the application of different structural systems for the slab of the studied model. The total waste generation during the construction phase of the different slab systems shows that the solid slab system produces the largest amount of construction waste, which is 101,361.385 kg. On the other hand, the void slab system generates 87,603.958 kg of the construction waste, which is the lowest amount among the four variables of this study. The additional purchasing costs due to the loss of construction materials indicate that the solid slab system would require 80,709.76 USD, which is the highest value of the four variables in this study. The void slab system would cost USD 50,054.12 for additional materials purchasing costs, which is approximately 38% lower than the solid slab system.